IPLC: Iterative Pseudo Label Correction Guided by SAM for Source-Free Domain Adaptation in Medical Image Segmentation

Source-Free Domain Adaptation (SFDA) is important for dealing with domain shift without access to source data and labels of target domain images for medical image segmentation. However, existing SFDA methods have limited performance due to insufficient supervision and unreliable pseudo labels. To address this issue, we propose a novel Iterative Pseudo Label Correction (IPLC) guided by the Segment Anything Model (SAM) SFDA framework for medical image segmentation. Specifically, with a pre-trained source model and SAM, we propose multiple random sampling and entropy estimation to obtain robust pseudo labels and mitigate the noise. We introduce mean negative curvature minimization to provide more sufficient constraints and achieve smoother segmentation. We also propose an Iterative Correction Learning (ICL) strategy to iteratively generate reliable pseudo labels with updated prompts for domain adaptation. Experiments on a public multi-site heart MRI segmentation dataset (M&MS) demonstrate that our method effectively improved the quality of pseudo labels and outperformed several state-of-the-art SFDA methods. The code is available at https://github.com/HiLab-git/IPLC .